Name
Abstract | ||
|---|---|---|
Methods to quantify cellular-level phenotypic differences between genetic groups are a key tool in genomics research. In disease processes such as cancer, phenotypic changes at the cellular level frequently manifest in the modification of cell population profiles. These changes are hard to detect due the ambiguity in identifying distinct cell phenotypes within a population. We present a methodology which enables the detection of such changes by generating a phenotypic signature of cell populations in a data-derived feature-space. Further, this signature is used to estimate a model for the redistribution of phenotypes that was induced by the genetic change. Results are presented on an experiment involving deletion of a tumor-suppressor gene dominant in breast cancer, where the methodology is used to detect changes in nuclear morphology between control and knockout groups. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-23629-7_42 | MICCAI (2) |
Keywords | Field | DocType |
genetic group,cellular-level phenotypic difference,cellular level,phenotypic signature,genetic change,breast cancer,phenotypic change,cellular phenotypes,distinct cell phenotypes,cell population,non-parametric population analysis,cell population profile,shape analysis | Population,Gene,Computer science,Genomics,Cell,Artificial intelligence,Computational biology,Cell nucleus,Disease,Pattern recognition,Phenotype,Bioinformatics,Cancer | Conference |
Volume | Issue | ISSN |
14 | Pt 2 | 0302-9743 |
Citations | PageRank | References |
2 | 0.39 | 5 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shantanu Singh | 1 | 31 | 5.06 |
| Firdaus Janoos | 2 | 100 | 9.58 |
| Thierry Pécot | 3 | 28 | 4.39 |
| Enrico Caserta | 4 | 5 | 1.46 |
| Kun Huang | 5 | 530 | 61.18 |
| Jens Rittscher | 6 | 686 | 67.07 |
| Gustavo Leone | 7 | 61 | 6.87 |
| Raghu Machiraju | 8 | 864 | 78.64 |